Replay-Based Continual Learning

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Replay-based continual learning studies how models can learn from a changing stream of tasks without losing what they learned earlier. Methods store and revisit selected examples, compressed features, prototypes, or generated data, often combining replay with distillation, class balancing, or adaptive scheduling. These approaches are used in settings such as class-incremental vision, language-model fine-tuning, robotics, reinforcement learning, and federated learning. Replay is a strong practical way to balance retention and adaptation, but its benefits depend on memory limits, replay quality and timing, and the cost or privacy risks of retaining past data.

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134 papers20 papers in the last four weeks, up 82% on the four weeks before. 0.1% of all new papers.

Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study
May 7, 2026Bomin Wang, Hangqi Zhou, Yibo Gao +1Interactive Medical Image Segmentation Stability-Plasticity Dilemma
School of Data Science, Fudan University, Shanghai, China.
CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
May 7, 2026Mei Wu, Wenchao Weng, Wenxin Su +2Contrastive Learning Traffic Flow Estimation
College of Computer Science, Shanghai Jiao Tong University, Shanghai 200240, China · College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310023, China · School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China
Replay-Based Continual Learning for Physics-Informed Neural Operators
May 6, 2026Yizheng Wang, Mohammad Sadegh Eshaghi, Xiaoying Zhuang +2OOD Generalization Replay-Based Continual Learning
Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China · Institute of Photonics, Department of Mathematics and Physics, Leibniz University Hannover, Germany · Institute of Structural Mechanics, Bauhaus-Universität Weimar, Marienstr. 15, D-99423 Weimar, Germany
Memory-Efficient Continual Learning with CLIP Models
May 5, 2026Ryan King, Gang Li, Bobak Mortazavi +1Class-Incremental Learning Contrastive Learning
Department of Computer Science Texas A&M University College Station, TX 77843 · Texas A&M University College Station, TX 77843
A Domain Incremental Continual Learning Benchmark for ICU Time Series Model Transportability
May 5, 2026Ryan King, Conrad Krueger, Ethan Veselka +2Clinical Outcome Prediction Domain-Incremental Learning
Computer Science & Engineering, Texas A&M University, College Station, United States
Adaptive Data Compression and Reconstruction for Memory-Bounded EEG Continual Learning
May 4, 2026Chengcheng XieElectroencephalography Continual Learning
Beihang University
Continual Learning of Feedback-based Molecular Communication
May 1, 2026Siddhant Setia, Junichi Suzuki, Tadashi NakanoContinual Learning Replay-Based Continual Learning
Department of Computer Science, University of Massachusetts, Boston Boston, MA, 02125, USA · Graduate School of Informatics, Osaka Metropolitan University Osaka, Japan
Continual Calibration: Coverage Can Collapse Before Accuracy in Lifelong LLM Fine-Tuning
Apr 27, 2026Ibne Farabi Shihab, Sanjeda Akter, Anuj SharmaContinual Learning for LLMs Conformal Prediction
Department of Computer Science, Iowa State University · Department of Civil, Construction & Environmental Engineering, Iowa State University
Lifecycle-Aware Federated Continual Learning in Mobile Autonomous Systems
Apr 22, 2026Beining Wu, Jun HuangMobile Robotics Federated Continual Learning
Department of Electrical Engineering and Computer Science, South Dakota State University, Brookings, 57006 SD
Recovery Guarantees for Continual Learning of Dependent Tasks: Memory, Data-Dependent Regularization, and Data-Dependent Weights
Apr 19, 2026Liangzu Peng, Uday Kiran Reddy Tadipatri, Ziqing Xu +2Continual Learning Replay-Based Continual Learning
University of Pennsylvania
Continual Hand-Eye Calibration for Open-world Robotic Manipulation
Apr 17, 2026Fazeng Li, Gan Sun, Chenxi Liu +3Hand-Eye Calibration Camera Pose Estimation
School of Automation Science and Engineering, South China University of Technology, Guangzhou 510641, China · State Key Laboratory of Robotics, the Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China · University of Chinese Academy of Sciences, Beijing 100049, China
Continual Learning for fMRI-Based Brain Disorder Diagnosis via Functional Connectivity Matrices Generative Replay
Apr 15, 2026Qianyu Chen, Shujian YuContinual Learning Replay-Based Continual Learning
†Nanyang Technological University · ‡VU Amsterdam · §UiT The Arctic University of Norway
Direct Discrepancy Replay: Distribution-Discrepancy Condensation and Manifold-Consistent Replay for Continual Face Forgery Detection
Apr 14, 2026Tianshuo Zhang, Haoyuan Zhang, Siran Peng +3Deepfake Detection Replay-Based Continual Learning
SAI, UCAS · MAIS, CASIA · IIE, CAS +2
Preventing Latent Rehearsal Decay in Online Continual SSL with SOLAR
Apr 12, 2026Giacomo Cignoni, Simone Magistri, Andrew D. Bagdanov +1Online Continual Learning Self-Supervised Learning
Department of Computer Science, University of Pisa, Pisa, Italy · Media Integration and Communication Center (MICC), University of Florence, Italy
Towards Lifelong Aerial Autonomy: Geometric Memory Management for Continual Visual Place Recognition in Dynamic Environments
Apr 10, 2026Xingyu Shao, Zhiqiang Yan, Liangzheng Sun +5Cross-View Geo-Localization Visual Place Recognition
Leveraging Complementary Embeddings for Replay Selection in Continual Learning with Small Buffers
Apr 9, 2026Danit Yanowsky, Daphna WeinshallExperience Replay Replay-Based Continual Learning
School of Computer Science and Engineering The Hebrew University of Jerusalem
From Selection to Scheduling: Federated Geometry-Aware Correction Makes Exemplar Replay Work Better under Continual Dynamic Heterogeneity
Apr 9, 2026Zhuang Qi, Ying-Peng Tang, Lei Meng +4Federated Continual Learning Replay-Based Continual Learning
School of Software, Shandong University, China · College of Computing and Data Science, Nanyang Technological University, Singapore · School of Computer Science and Technology, Harbin Institute of Technology, China
Temporal Memory for Resource-Constrained Agents: Continual Learning via Stochastic Compress-Add-Smooth
Mar 31, 2026Michael ChertkovMemory Compression Continual Learning
Graduate Inter-Disciplinary Program in Applied Mathematics, & Department of Mathematics, University Arizona, Tucson, AZ 85721
APEX-EM: Non-Parametric Online Learning for Autonomous Agents via Structured Procedural-Episodic Experience Replay
Mar 31, 2026Pratyay Banerjee, Masud Moshtaghi, Ankit ChadhaReplay-Based Continual Learning Persistent Memory for Language Models
Amazon, AGI / Sunnyvale, USA
FeDMRA: Federated Incremental Learning with Dynamic Memory Replay Allocation
Mar 30, 2026Tiantian Wang, Xiang Xiang, Simon S. DuFederated Continual Learning Federated Learning
Huazhong University of Science and Technology, Wuhan, China · University of Washington, Seattle, USA
A Comparative Empirical Study of Catastrophic Forgetting Mitigation in Sequential Task Adaptation for Continual Natural Language Processing Systems
Mar 19, 2026Aram Abrahamyan, Sachin KumarCatastrophic Forgetting Replay-Based Continual Learning
Zaven P. and Sonia Akian College of Science and Engineering, American University of Armenia, Yerevan 0019, Armenia
MedCL-Bench: Benchmarking stability-efficiency trade-offs and scaling in biomedical continual learning
Mar 17, 2026Min Zeng, Shuang Zhou, Zaifu Zhan +1Continual Learning Continual Learning for LLMs
Division of Computational Health Sciences, Department of Surgery, University of Minnesota, Minneapolis, 55455, MN, USA.
Memory-efficient Continual Learning with Prototypical Exemplar Condensation
Mar 14, 2026Minh-Duong Nguyen, Thien-Thanh Dao, Le-Tuan Nguyen +2Replay-Based Continual Learning Memory-Efficient Training
College of Engineering and Computer Science, VinUniversity, Hanoi, Vietnam · Center for AI Research, VinUniversity, Hanoi, Vietnam · Faculty of Information Systems, Phenikaa University, Hanoi, Vietnam +1
ARROW: Augmented Replay for RObust World models
Mar 12, 2026Abdulaziz Alyahya, Abdallah Al Siyabi, Markus R. Ernst +3Continual RL Replay-Based Continual Learning
Department of Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU) · Department of Data Science & AI, Faculty of Information Technology, Monash University · School of Computer Science and Engineering, Faculty of Engineering, University of New South Wales, Sydney
Lifelong Imitation Learning with Multimodal Latent Replay and Incremental Adjustment
Mar 11, 2026Fanqi Yu, Matteo Tiezzi, Tommaso Apicella +2Imitation Learning Robot Imitation Learning
AI for Good (AIGO), Istituto Italiano di Tecnologia, Genoa, Italy · DITEN, University of Genoa, Genoa, Italy · PAVIS, Istituto Italiano di Tecnologia, Genoa, Italy +1
MSSR: Memory-Aware Adaptive Replay for Continual LLM Fine-Tuning
Mar 10, 2026Yiyang Lu, Yu He, Jianlong Chen +1Continual Learning for LLMs Replay-Based Continual Learning
The Chinese University of Hong Kong, Shenzhen · Nanjing University
Pretrained Vision-Language-Action Models are Surprisingly Resistant to Forgetting in Continual Learning
Mar 4, 2026Huihan Liu, Changyeon Kim, Bo Liu +2Continual Robot Learning Continual Learning for VLA Models
The University of Texas at Austin · KAIST · Microsoft Superintelligence.
IDER: IDempotent Experience Replay for Reliable Continual Learning
Feb 28, 2026Zhanwang Liu, Yuting Li, Haoyuan Gao +4Continual Learning Catastrophic Forgetting
School of Computer Science, Shanghai Jiao Tong University · State Key Laboratory of General Artificial Intelligence, BIGAI · Lehigh University +1
Quantifying Catastrophic Forgetting in IoT Intrusion Detection Systems
Feb 27, 2026Sourasekhar Banerjee, David Bergqvist, Salman Toor +2IoT Intrusion Detection Catastrophic Forgetting
Uppsala University, Department of Information Technology, Sweden · Scaleout Systems AB, Sweden · Ericsson Research, Research Area AI, Sweden
CoPeP: Benchmarking Continual Pretraining for Protein Language Models
Feb 27, 2026Darshan Patil, Pranshu Malviya, Mathieu Reymond +2Protein Language Models Replay-Based Continual Learning
Mila - Quebec AI Institute · Polytechnique Montréal · Chandar Research Lab +1

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Beyond Forgetting in Continual Medical Image Segmentation: A Comprehensive Benchmark Study

Bomin Wang, Hangqi Zhou, Yibo Gao, Xiahai Zhuang

School of Data Science, Fudan University, Shanghai, China.

Interactive Medical Image Segmentation Stability-Plasticity Dilemma Replay-Based Continual Learning Medical Image Segmentation Medical Image Benchmarks

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Continual learning (CL) is essential for deploying medical image segmentation models in clinical environments where imaging domains, anatomical targets, and diagnostic tasks evolve over time. However, continual segmentation still faces three main challenges. First, the scenarios for this task remain insufficiently standardized for real-world clinical settings. Second, existing research has been primarily focused on mitigating forgetting, overlooking the other essential properties such as plasticity. Third, a benchmark work with comprehensive evaluation on existing methods is stll desirable. To address these gaps, we present such benchmark study of continual medical image segmentation. We first define three clinically motivated scenarios, namely Domain-CL, Class-CL, and Organ-CL, to respectively capture the cross-center domain shift, the incremental anatomical structure segmentation, and the cross-organ segmentation. We then introduce an evaluation framework that measures not only general performance and forgetting, but also plasticity, forward generalizability, parameter efficiency, and replay burden. The results, from extensive experiments with representative CL methods, showed that it was still challenging to develop a model that could satisfy all the requirements simultaneously. Nevertheless, these studies also suggested that the replay-based methods achieve the best overall balance between stability and plasticity, the parameter-isolation methods should be effective at reducing forgetting, though at the cost of increased model size, and the forward generalizability remain a significantly understudied aspect of this research field. Finally, we discuss related learning paradigms and outline future directions for continual medical image segmentation.